Molecular regulation of conditioning film formation and quorum quenching in sulfate reducing bacteria

Influence of Copper on Oleidesulfovibrio alaskensis G20 Biofilm Formation

Copper is known to have toxic effects on bacterial growth. This study aimed to determine the influence of copper ions on Oleidesulfovibrio alaskensis G20 biofilm formation in a lactate-C medium supplemented with variable copper ion concentrations. OA G20, when grown in media supplemented with high copper ion concentrations of 5, 15, and 30 µM, exhibited inhibited growth in its planktonic state. Conversely, under similar copper concentrations, OA G20 demonstrated enhanced biofilm formation on glass coupons. Microscopic studies revealed that biofilms exposed to copper stress demonstrated a change in cellular morphology and more accumulation of carbohydrates and proteins than controls. Consistent with these findings, sulfur (dsrA, dsrB, sat, aprA) and electron transport (NiFeSe, NiFe, ldh, cyt3) genes, polysaccharide synthesis (poI), and genes involved in stress response (sodB) were significantly upregulated in copper-induced biofilms, while genes (ftsZ, ftsA, ftsQ) related to cellular division were negatively regulated compared to controls. These results indicate that the presence of copper ions triggers alterations in cellular morphology and gene expression levels in OA G20, impacting cell attachment and EPS production. This adaptation, characterized by increased biofilm formation, represents a crucial strategy employed by OA G20 to resist metal ion stress.

Volatilome: Smells like microbial spirit

In recent years, the study of volatile compounds has sparked interest due to their implications in signaling and the enormous variety of bioactive properties attributed to them. Despite the absence of analysis methods standardization, there are a multitude of tools and databases that allow the identification and quantification of volatile compounds. These compounds are chemically heterogeneous and their diverse properties are exploited by various fields such as cosmetics, the food industry, agriculture and medicine, some of which will be discussed here. In virtue of volatile compounds being ubiquitous and fast chemical messengers, these molecules mediate a large number of interspecific and intraspecific interactions, which are key at an ecological level to maintaining the balance and correct functioning of ecosystems. This review briefly summarized the role of volatile compounds in inter- and intra-specific relationships as well as industrial applications associated with the use of these compounds that is emerging as a promising field of study.

Transcriptome-wide marker gene expression analysis of stress-responsive sulfate-reducing bacteria

Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz., transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of Desulfovibrio spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of Oleidesulfovibrio alaskensis (OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation.

Convergence research and training in computational bioengineering: a case study on AI/ML driven biofilm-material interaction discovery

Initially, research disciplines operated independently, but the emergence of trans-disciplinary sciences led to convergence research, impacting graduate programs and research laboratories, especially in bioengineering and material engineering as presented here. Current graduate curriculum fails to efficiently prepare students for multidisciplinary and convergence research, thus creating a gap between the students and research laboratory expectations. We present a convergence training framework for graduate students, incorporating problem-based learning under the guidance of senior scientists and collaboration with postdoctoral researchers. This case study serves as a template for transdisciplinary convergent training projects - bridging the expertise gap and fostering successful convergence learning experiences in computational biointerface (material-biology interface). The 18-month Advanced Data Science Workshop, initiated in 2019, involves project-based learning, online training modules, and data collection. A pilot solution utilized Jupyter notebook on Google collaborator and culminated in a face-to-face workshop where project presentations and finalization occurred. The program started with 9 experts in the four diverse fields creating 14 curated projects in data science (Artificial Intelligence/Machine Learning), material science, biofilm engineering, and biointerface. These were integrated into convergence research through webinars by the experts. The experts chose 8 of the 14 projects to be part of an all-day in-person workshop, where over 20 learners formed eight teams that tackled complex problems at the interface of digital image processing, gene expression analysis, and material prediction. Each team was comprised of students and postdoctoral researchers or research scientists from diverse domains including computer science, materials science, and biofilm research. Some projects were selected for presentation at the international IEEE Bioinformatics conference in 2022, with three resulting Machine Learning (ML) models submitted as a journal paper. Students engaged in problem discussions, collaborated with experts from different disciplines, and received guidance in decomposing learning objectives. Based on learner feedback, this successful experience allows for consolidation and integration of convergence research via problem-based learning into the curriculum. Three bioengineering participants, who received training in data science and engineering, have received bioinformatics jobs in biotechnology industries.